Custom compute cores in integrated circuit devices

ABSTRACT

A system includes a processor and a hardware accelerator coupled to the processor. The hardware accelerator includes data analysis elements configured to analyze a data stream based on configuration data and to output a result, and an integrated circuit device that includes a DMA engine that writes input data to and read output data from the data analysis elements, one or more preprocessing cores that receive the input data from the DMA engine prior to the DMA engine writing the input data to the one or more data analysis elements and perform custom preprocessing functions on the input data, and one or more post-processing cores that receive the output data from the DMA engine after the output data is read from the data analysis elements but prior to the output data being output to the processor and perform custom post-processing functions on the output data.

CROSS-REFERENCE TO RELATED APPLICATION

This present application is a continuation application of U.S. application Ser. No. 15/409,351 entitled “CUSTOM COMPUTE CORES IN INTEGRATED CIRCUIT DEVICES,” filed Jan. 18, 2017, which is a Non-Provisional Patent Application of U.S. Provisional Patent Application No. 62/410,732, entitled “CUSTOM COMPUTE CORES IN INTEGRATED CIRCUIT DEVICES,” filed Oct. 20, 2016, which is herein incorporated by reference in its entirety and for all purposes.

BACKGROUND Field of Invention

Embodiments of the invention relate generally to electronic devices and, more specifically, in certain embodiments, to custom compute cores that provide interfacing functionality with electronic devices used for data analysis.

Description of Related Art

Complex pattern recognition can be inefficient to perform on a conventional von Neumann based computer. A biological brain, in particular a human brain, however, is adept at performing pattern recognition. Current research suggests that a human brain performs pattern recognition using a series of hierarchically organized neuron layers in the neocortex. Neurons in the lower layers of the hierarchy analyze “raw signals” from, for example, sensory organs, while neurons in higher layers analyze signal outputs from neurons in the lower levels. This hierarchical system in the neocortex, possibly in combination with other areas of the brain, accomplishes the complex pattern recognition that enables humans to perform high level functions such as spatial reasoning, conscious thought, and complex language.

In the field of computing, pattern recognition tasks are increasingly challenging. Ever larger volumes of data are transmitted between computers, and the number of patterns that users wish to identify is increasing. For example, spam or malware are often detected by searching for patterns in a data stream, e.g., particular phrases or pieces of code. The number of patterns increases with the variety of spam and malware, as new patterns may be implemented to search for new variants. Searching a data stream for each of these patterns can form a computing bottleneck. Often, as the data stream is received, it is searched for each pattern, one at a time. The delay before the system is ready to search the next portion of the data stream increases with the number of patterns. Thus, pattern recognition may slow the receipt of data.

Hardware has been designed to search a data stream for patterns, but this hardware often is unable to process adequate amounts of data in an amount of time given. Some devices configured to search a data stream do so by distributing the data stream among a plurality of circuits. The circuits each determine whether the data stream matches a portion of a pattern. Often, a large number of circuits operate in parallel, each searching the data stream at generally the same time. The system may then further process the results from these circuits, to arrive at the final results. These “intermediate results”, however, can be larger than the original input data, which may pose issues for the system. The ability to use a cascaded circuits approach, similar to the human brain, offers one potential solution to this problem. However, there has not been a system that effectively allows for performing pattern recognition in a manner more comparable to that of a biological brain. In addition, there may be unused resources on devices included in such systems, and there may be functionality that is desirable to enhance and/or modify one or more aspects of the system. Accordingly, development of a system that performs pattern recognition comparable to the biological brain and that more efficiently uses device resources to provide certain functionality is desired.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of system having a state machine engine, according to various embodiments;

FIG. 2 illustrates an example of an FSM lattice of the state machine engine of FIG. 1, according to various embodiments;

FIG. 3 illustrates an example of a block of the FSM lattice of FIG. 2, according to various embodiments;

FIG. 4 illustrates an example of a row of the block of FIG. 3, according to various embodiments;

FIG. 4A illustrates a block as in FIG. 3 having counters in rows of the block, according to various embodiments of the invention;

FIG. 5 illustrates an example of a Group of Two of the row of FIG. 4, according to embodiments;

FIG. 6 illustrates an example of a finite state machine graph, according to various embodiments;

FIG. 7 illustrates an example of two-level hierarchy implemented with FSM lattices, according to various embodiments;

FIG. 7A illustrates a second example of two-level hierarchy implemented with FSM lattices, according to various embodiments;

FIG. 8 illustrates an example of a method for a compiler to convert source code into a binary file for programming of the FSM lattice of FIG. 2, according to various embodiments;

FIG. 9 illustrates a state machine engine, according to various embodiments;

FIG. 10 illustrates an example of a method for an integrated circuit device to receive and implement one or more custom compute cores, according to various embodiments;

FIG. 11 illustrates an example of an integrated circuit device interfacing between the processor and the state machine engine, according to various embodiments;

FIG. 12 illustrates example components of the integrated circuit device of FIG. 11, according to various embodiments;

FIG. 13 illustrates an example of a method for the integrated circuit device to perform functionality provided by the custom compute cores during runtime, according to various embodiments; and

FIG. 14 illustrates an example of a method for the processor and the integrated circuit device to cooperatively process data, according to various embodiments.

DETAILED DESCRIPTION

Turning now to the figures, FIG. 1 illustrates an embodiment of a processor-based system, generally designated by reference numeral 10. The system 10 may be any of a variety of types such as a desktop computer, laptop computer, pager, cellular phone, personal organizer, portable audio player, control circuit, camera, etc. The system 10 may also be a network node, such as a router, a server, or a client (e.g., one of the previously-described types of computers). The system 10 may be some other sort of electronic device, such as a copier, a scanner, a printer, a game console, a television, a set-top video distribution or recording system, a cable box, a personal digital media player, a factory automation system, an automotive computer system, or a medical device. (The terms used to describe these various examples of systems, like many of the other terms used herein, may share some referents and, as such, should not be construed narrowly in virtue of the other items listed.)

In a typical processor-based device, such as the system 10, a processor 12, such as a microprocessor, controls the processing of system functions and requests in the system 10. Further, the processor 12 may comprise a plurality of processors that share system control. The processor 12 may be coupled directly or indirectly to each of the elements in the system 10, such that the processor 12 controls the system 10 by executing instructions that may be stored within the system 10 or external to the system 10.

In accordance with the embodiments described herein, the system 10 includes an integrated circuit device 13 and a state machine engine 14. The integrated circuit device 13 and the state machine engine 14 may be disposed on the same hardware accelerator card 15 (e.g., peripheral component interconnect express (PCIe) accelerator card). The state machine engine 14 may operate under the control of the processor 12. As such, the processor 12 and the state machine engine 14 may be in communication via the integrated circuit device 13, which may function as a translator and controller. The integrated circuit device 13 may include any suitable programmable logic device, such as a field programmable gate array (FPGA). In some embodiments, the integrated circuit device 13 may implement a base build (e.g., firmware) that functions as a bridge between a PCIe interface used by the processor 12 and a double data rate (DDR) interface used by the state machine engine 14. More specifically, the base build may allow register mapping access from PCIe to a DDR register map.

Performing translation from PCIe to DDR does not use a substantial amount of logic, and therefore, does not use many resources of the integrated circuit device 13. Further, fairly large integrated circuit devices 13 may be placed on PCIe accelerator cards that use the state machine engine 14 to share a certain number of IO ports. As a result, unused resources may be present in the integrated circuit devices 13. Also, in some instances, the processor 12 may perform tasks that delay its processing throughput performance, such as pre-processing data to be sent to the state machine engine 14 and/or post-processing data received from the state machine engine 14. Accordingly, some embodiments of the present disclosure relate to freeing up the processor 12 by implementing custom compute cores 17 in the unused space in the integrated circuit device 13. The custom compute cores 17 may refer to custom logic that programs the selected portion of the integrated circuit device 13 to perform the logic when referenced. Thus, the programmed resources of the integrated circuit device 13 become custom hardware modules (e.g., firmware) after implementation of the custom compute cores 17.

That is, some embodiments may enable users to build their own instruction sets in custom compute cores 17 to perform functions within the integrated circuit device 13. An interface specification defines how the custom compute core functions can interface to the existing base build, pipeline, and integrated circuit device driver. The specification defines a usable address map and physical periphery interface for direct memory access (DMA) and data management. The physical periphery interface for DMA may expose certain functions, such as read from and/or write to the state machine engine 14, for reference in logic included in one or more custom compute cores 17. As such, a software development kit (SDK) application programming interface (API) may be used by a user to define register-transfer level (RTL) or open computing language (OpenCL) descriptions of the custom compute core functions to target the unused programmable space of the integrated circuit device 13 using the specification. The SDK API may also be used to access the custom compute cores 17 directly or to insert the custom compute cores 17 into the data path of the integrated circuit device 13 for processing input data (e.g., symbols) from the processor 12 or for interpreting output data (e.g., event vector results) from the state machine engine 14.

As described further below, each custom compute core 17 may include one or more pre-processing cores or post-processing cores. It should be understood that one or more custom compute cores 17 may be implemented into the integrated circuit device 13 to perform any number of suitable custom functions. In some embodiments, the pre-processing cores may execute their functionality using the data received from the processor 12 prior to sending the pre-processed data to the state machine engine 14. In some embodiments, the post-processing cores may execute their functionality using the data received from the state machine engine 14 prior to sending the post-processed data to the processor 12. The functions performed by the pre-processing and/or post-processing cores may include data compressing, organizing, sorting, merging, deleting, modifying, inserting, segmenting, filtering, or the like.

It should be understood that the custom compute cores 17 may alleviate bandwidth and/or processing issues of the processor 12 by absorbing some of its burdensome functionality. For example, data to be searched by the state machine engine 14 may involve a large and/or complex database. Thus, in some instances, the input data may be compressed by the processor 12 prior to transmission. However, performing compression on all of the data may form a processing throughput performance bottleneck at the processor 12. Accordingly, a pre-processing core that performs data compression may be developed and integrated into the resource fabric of the integrated circuit device 13 to enable the processor 12 to send the data without compressing it. As a result, the processor 12 is freed to perform other functions while the data is still compressed prior to transmission to the state machine engine 14, albeit by the pre-processing core of the integrated circuit device 13.

In some embodiments, the state machine engine 14 may employ any one of a number of state machine architectures, including, but not limited to Mealy architectures, Moore architectures, Finite State Machines (FSMs), Deterministic FSMs (DFSMs), Bit-Parallel State Machines (BPSMs), etc. Though a variety of architectures may be used, for discussion purposes, the application refers to FSMs. However, those skilled in the art will appreciate that the described techniques may be employed using any one of a variety of state machine architectures.

As discussed further below, the state machine engine 14 may include a number of (e.g., one or more) finite state machine (FSM) lattices (e.g., core of a chip). For purposes of this application the term “lattice” refers to an organized framework (e.g., routing matrix, routing network, frame) of elements (e.g., Boolean cells, counter cells, state machine elements, state transition elements). Furthermore, the “lattice” may have any suitable shape, structure, or hierarchical organization (e.g., grid, cube, spherical, cascading). Each FSM lattice may implement multiple FSMs that each receive and analyze the same data in parallel. Further, the FSM lattices may be arranged in groups (e.g., clusters), such that clusters of FSM lattices may analyze the same input data in parallel. Further, clusters of FSM lattices of the state machine engine 14 may be arranged in a hierarchical structure wherein outputs from state machine lattices on a lower level of the hierarchical structure may be used as inputs to state machine lattices on a higher level. By cascading clusters of parallel FSM lattices of the state machine engine 14 in series through the hierarchical structure, increasingly complex patterns may be analyzed (e.g., evaluated, searched, etc.).

Further, based on the hierarchical parallel configuration of the state machine engine 14, the state machine engine 14 can be employed for complex data analysis (e.g., pattern recognition or other processing) in systems that utilize high processing speeds. For instance, embodiments described herein may be incorporated in systems with processing speeds of 1 GByte/sec. Accordingly, utilizing the state machine engine 14, data from high speed memory devices or other external devices may be rapidly analyzed. The state machine engine 14 may analyze a data stream according to several criteria (e.g., search terms), at about the same time, e.g., during a single device cycle. Each of the FSM lattices within a cluster of FSMs on a level of the state machine engine 14 may each receive the same search term from the data stream at about the same time, and each of the parallel FSM lattices may determine whether the term advances the state machine engine 14 to the next state in the processing criterion. The state machine engine 14 may analyze terms according to a relatively large number of criteria, e.g., more than 100, more than 110, or more than 10,000. Because they operate in parallel, they may apply the criteria to a data stream having a relatively high bandwidth, e.g., a data stream of greater than or generally equal to 1 GByte/sec, without slowing the data stream.

In one embodiment, the state machine engine 14 may be configured to recognize (e.g., detect) a great number of patterns in a data stream. For instance, the state machine engine 14 may be utilized to detect a pattern in one or more of a variety of types of data streams that a user or other entity might wish to analyze. For example, the state machine engine 14 may be configured to analyze a stream of data received over a network, such as packets received over the Internet or voice or data received over a cellular network. In one example, the state machine engine 14 may be configured to analyze a data stream for spam or malware. The data stream may be received as a serial data stream, in which the data is received in an order that has meaning, such as in a temporally, lexically, or semantically significant order. Alternatively, the data stream may be received in parallel or out of order and, then, converted into a serial data stream, e.g., by reordering packets received over the Internet. In some embodiments, the data stream may present terms serially, but the bits expressing each of the terms may be received in parallel. The data stream may be received from a source external to the system 10, or may be formed by interrogating a memory device, such as the memory 16, and forming the data stream from data stored in the memory 16. In other examples, the state machine engine 14 may be configured to recognize a sequence of characters that spell a certain word, a sequence of genetic base pairs that specify a gene, a sequence of bits in a picture or video file that form a portion of an image, a sequence of bits in an executable file that form a part of a program, or a sequence of bits in an audio file that form a part of a song or a spoken phrase. The stream of data to be analyzed may include multiple bits of data in a binary format or other formats, e.g., base ten, ASCII, etc. The stream may encode the data with a single digit or multiple digits, e.g., several binary digits.

As will be appreciated, the system 10 may include memory 16. The memory 16 may include volatile memory, such as Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Synchronous DRAM (SDRAM), Double Data Rate DRAM (DDR SDRAM), DDR2 SDRAM, DDR3 SDRAM, etc. The memory 16 may also include non-volatile memory, such as read-only memory (ROM), PC-RAM, silicon-oxide-nitride-oxide-silicon (SONOS) memory, metal-oxide-nitride-oxide-silicon (MONOS) memory, polysilicon floating gate based memory, and/or other types of flash memory of various architectures (e.g., NAND memory, NOR memory, etc.) to be used in conjunction with the volatile memory. The memory 16 may include one or more memory devices, such as DRAM devices, that may provide data to be analyzed by the state machine engine 14. As used herein, the term “provide” may generically refer to direct, input, insert, issue, route, send, transfer, transmit, generate, give, make available, move, output, pass, place, read out, write, etc. Such devices may be referred to as or include solid state drives (SSD's), MultiMediaCards (MMC's), SecureDigital (SD) cards, CompactFlash (CF) cards, or any other suitable device. Further, it should be appreciated that such devices may couple to the system 10 via any suitable interface, such as Universal Serial Bus (USB), Peripheral Component Interconnect (PCI), PCI Express (PCI-E), Small Computer System Interface (SCSI), IEEE 1394 (Firewire), or any other suitable interface. To facilitate operation of the memory 16, such as the flash memory devices, the system 10 may include a memory controller (not illustrated). As will be appreciated, the memory controller may be an independent device or it may be integral with the processor 12. Additionally, the system 10 may include an external storage 18, such as a magnetic storage device. The external storage may also provide input data to the state machine engine 14.

The system 10 may include a number of additional elements. For instance, a compiler 20 may be used to configure (e.g., program) the state machine engine 14, as described in more detail with regard to FIG. 8. An input device 22 may also be coupled to the processor 12 to allow a user to input data into the system 10. For instance, an input device 22 may be used to input data into the memory 16 for later analysis by the state machine engine 14. The input device 22 may include buttons, switching elements, a keyboard, a light pen, a stylus, a mouse, and/or a voice recognition system, for instance. An output device 24, such as a display may also be coupled to the processor 12. The display 24 may include an LCD, a CRT, LEDs, and/or an audio display, for example. They system may also include a network interface device 26, such as a Network Interface Card (NIC), for interfacing with a network, such as the Internet. As will be appreciated, the system 10 may include many other components, depending on the application of the system 10.

FIGS. 2-5 illustrate an example of a FSM lattice 30. In an example, the FSM lattice 30 comprises an array of blocks 32. As will be described, each block 32 may include a plurality of selectively couple-able hardware elements (e.g., configurable elements and/or special purpose elements) that correspond to a plurality of states in a FSM. Similar to a state in a FSM, a hardware element can analyze an input stream and activate a downstream hardware element, based on the input stream.

The configurable elements can be configured (e.g., programmed) to implement many different functions. For instance, the configurable elements may include state transition elements (STEs) 34, 36 (shown in FIG. 5) that function as data analysis elements and are hierarchically organized into rows 38 (shown in FIGS. 3 and 4) and blocks 32 (shown in FIGS. 2 and 3). The STEs each may be considered an automaton, e.g., a machine or control mechanism designed to follow automatically a predetermined sequence of operations or respond to encoded instructions. Taken together, the STEs form an automata processor as state machine engine 14. To route signals between the hierarchically organized STEs 34, 36, a hierarchy of configurable switching elements can be used, including inter-block switching elements 40 (shown in FIGS. 2 and 3), intra-block switching elements 42 (shown in FIGS. 3 and 4) and intra-row switching elements 44 (shown in FIG. 4).

As described below, the switching elements may include routing structures and buffers. A STE 34, 36 can correspond to a state of a FSM implemented by the FSM lattice 30. The STEs 34, 36 can be coupled together by using the configurable switching elements as described below. Accordingly, a FSM can be implemented on the FSM lattice 30 by configuring the STEs 34, 36 to correspond to the functions of states and by selectively coupling together the STEs 34, 36 to correspond to the transitions between states in the FSM.

FIG. 2 illustrates an overall view of an example of a FSM lattice 30. The FSM lattice 30 includes a plurality of blocks 32 that can be selectively coupled together with configurable inter-block switching elements 40. The inter-block switching elements 40 may include conductors 46 (e.g., wires, traces, etc.) and buffers 48, 50. In an example, buffers 48 and 50 are included to control the connection and timing of signals to/from the inter-block switching elements 40. As described further below, the buffers 48 may be provided to buffer data being sent between blocks 32, while the buffers 50 may be provided to buffer data being sent between inter-block switching elements 40. Additionally, the blocks 32 can be selectively coupled to an input block 52 (e.g., a data input port) for receiving signals (e.g., data) and providing the data to the blocks 32. The blocks 32 can also be selectively coupled to an output block 54 (e.g., an output port) for providing signals from the blocks 32 to an external device (e.g., another FSM lattice 30). The FSM lattice 30 can also include a programming interface 56 to configure (e.g., via an image, program) the FSM lattice 30. The image can configure (e.g., set) the state of the STEs 34, 36. For example, the image can configure the STEs 34, 36 to react in a certain way to a given input at the input block 52. For example, a STE 34, 36 can be set to output a high signal when the character ‘a’ is received at the input block 52.

In an example, the input block 52, the output block 54, and/or the programming interface 56 can be implemented as registers such that writing to or reading from the registers provides data to or from the respective elements. Accordingly, bits from the image stored in the registers corresponding to the programming interface 56 can be loaded on the STEs 34, 36. Although FIG. 2 illustrates a certain number of conductors (e.g., wire, trace) between a block 32, input block 52, output block 54, and an inter-block switching element 40, it should be understood that in other examples, fewer or more conductors may be used.

FIG. 3 illustrates an example of a block 32. A block 32 can include a plurality of rows 38 that can be selectively coupled together with configurable intra-block switching elements 42. Additionally, a row 38 can be selectively coupled to another row 38 within another block 32 with the inter-block switching elements 40. A row 38 includes a plurality of STEs 34, 36 organized into pairs of configurable elements that are referred to herein as groups of two (GOTs) 60. In an example, a block 32 comprises sixteen (16) rows 38.

FIG. 4 illustrates an example of a row 38. A GOT 60 can be selectively coupled to other GOTs 60 and any other elements (e.g., a special purpose element 58) within the row 38 by configurable intra-row switching elements 44. A GOT 60 can also be coupled to other GOTs 60 in other rows 38 with the intra-block switching element 42, or other GOTs 60 in other blocks 32 with an inter-block switching element 40. In an example, a GOT 60 has a first and second input 62, 64, and an output 66. The first input 62 is coupled to a first STE 34 of the GOT 60 and the second input 64 is coupled to a second STE 36 of the GOT 60, as will be further illustrated with reference to FIG. 5.

In an example, the row 38 includes a first and second plurality of row interconnection conductors 68, 70. In an example, an input 62, 64 of a GOT 60 can be coupled to one or more row interconnection conductors 68, 70, and an output 66 can be coupled to one or more row interconnection conductor 68, 70. In an example, a first plurality of the row interconnection conductors 68 can be coupled to each STE 34, 36 of each GOT 60 within the row 38. A second plurality of the row interconnection conductors 70 can be coupled to only one STE 34, 36 of each GOT 60 within the row 38, but cannot be coupled to the other STE 34, 36 of the GOT 60. In an example, a first half of the second plurality of row interconnection conductors 70 can couple to first half of the STEs 34, 36 within a row 38 (one STE 34 from each GOT 60) and a second half of the second plurality of row interconnection conductors 70 can couple to a second half of the STEs 34, 36 within a row 38 (the other STE 34, 36 from each GOT 60), as will be better illustrated with respect to FIG. 5. The limited connectivity between the second plurality of row interconnection conductors 70 and the STEs 34, 36 is referred to herein as “parity”. In an example, the row 38 can also include a special purpose element 58 such as a counter, a configurable Boolean logic element, look-up table, RAM, a field configurable gate array (FPGA), an application specific integrated circuit (ASIC), a configurable processor (e.g., a microprocessor), or other element for performing a special purpose function.

In an example, the special purpose element 58 comprises a counter (also referred to herein as counter 58). In an example, the counter 58 comprises a 12-bit configurable down counter. The 12-bit configurable counter 58 has a counting input, a reset input, and zero-count output. The counting input, when asserted, decrements the value of the counter 58 by one. The reset input, when asserted, causes the counter 58 to load an initial value from an associated register. For the 12-bit counter 58, up to a 12-bit number can be loaded in as the initial value. When the value of the counter 58 is decremented to zero (0), the zero-count output is asserted. The counter 58 also has at least two modes, pulse and hold. When the counter 58 is set to pulse mode, the zero-count output is asserted when the counter 58 reaches zero. For example, the zero-count output is asserted during the processing of an immediately subsequent next data byte, which results in the counter 58 being offset in time with respect to the input character cycle. After the next character cycle, the zero-count output is no longer asserted. In this manner, for example, in the pulse mode, the zero-count output is asserted for one input character processing cycle. When the counter 58 is set to hold mode the zero-count output is asserted during the clock cycle when the counter 58 decrements to zero, and stays asserted until the counter 58 is reset by the reset input being asserted.

In another example, the special purpose element 58 comprises Boolean logic. For example, the Boolean logic may be used to perform logical functions, such as AND, OR, NAND, NOR, Sum of Products (SoP), Negated-Output Sum of Products (NSoP), Negated-Output Product of Sum (NPoS), and Product of Sums (PoS) functions. This Boolean logic can be used to extract data from terminal state STEs (corresponding to terminal nodes of a FSM, as discussed later herein) in FSM lattice 30. The data extracted can be used to provide state data to other FSM lattices 30 and/or to provide configuring data used to reconfigure FSM lattice 30, or to reconfigure another FSM lattice 30.

FIG. 4A is an illustration of an example of a block 32 having rows 38 which each include the special purpose element 58. For example, the special purpose elements 58 in the block 32 may include counter cells 58A and Boolean logic cells 58B. While only the rows 38 in row positions 0 through 4 are illustrated in FIG. 4A (e.g., labeled 38A through 38E), each block 32 may have any number of rows 38 (e.g., 16 rows 38), and one or more special purpose elements 58 may be configured in each of the rows 38. For example, in one embodiment, counter cells 58A may be configured in certain rows 38 (e.g., in row positions 0, 4, 8, and 12), while the Boolean logic cells 58B may be configured in the remaining of the 16 rows 38 (e.g., in row positions 1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15, and 16). The GOT 60 and the special purpose elements 58 may be selectively coupled (e.g., selectively connected) in each row 38 through intra-row switching elements 44, where each row 38 of the block 32 may be selectively coupled with any of the other rows 38 of the block 32 through intra-block switching elements 42.

In some embodiments, each active GOT 60 in each row 38 may output a signal indicating whether one or more conditions are detected (e.g., a search result is detected), and the special purpose element 58 in the row 38 may receive the GOT 60 output to determine whether certain quantifiers of the one or more conditions are met and/or count a number of times a condition is detected. For example, quantifiers of a count operation may include determining whether a condition was detected at least a certain number of times, determining whether a condition was detected no more than a certain number of times, determining whether a condition was detected exactly a certain number of times, and determining whether a condition was detected within a certain range of times.

Outputs from the counter 58A and/or the Boolean logic cell 58B may be communicated through the intra-row switching elements 44 and the intra-block switching elements 42 to perform counting or logic with greater complexity. For example, counters 58A may be configured to implement the quantifiers, such as asserting an output only when a condition is detected an exact number of times. Counters 58A in a block 32 may also be used concurrently, thereby increasing the total bit count of the combined counters to count higher numbers of a detected condition. Furthermore, in some embodiments, different special purpose elements 58 such as counters 58A and Boolean logic cells 58B may be used together. For example, an output of one or more Boolean logic cells 58B may be counted by one or more counters 58A in a block 32.

FIG. 5 illustrates an example of a GOT 60. The GOT 60 includes a first STE 34, a second STE 36, and intra-group circuitry 37 coupled to the first STE 34 and the second STE 36. For example, the first STE 34 and the second STE 36 may have inputs 62, 64 and outputs 72, 74 coupled to an OR gate 76 and a 3-to-1 multiplexer 78 of the intra-group circuitry 37. The 3-to-1 multiplexer 78 can be set to couple the output 66 of the GOT 60 to either the first STE 34, the second STE 36, or the OR gate 76. The OR gate 76 can be used to couple together both outputs 72, 74 to form the common output 66 of the GOT 60. In an example, the first and second STE 34, 36 exhibit parity, as discussed above, where the input 62 of the first STE 34 can be coupled to some of the row interconnection conductors 68 and the input 64 of the second STE 36 can be coupled to other row interconnection conductors 70 the common output 66 may be produced which may overcome parity problems. In an example, the two STEs 34, 36 within a GOT 60 can be cascaded and/or looped back to themselves by setting either or both of switching elements 79. The STEs 34, 36 can be cascaded by coupling the output 72, 74 of the STEs 34, 36 to the input 62, 64 of the other STE 34, 36. The STEs 34, 36 can be looped back to themselves by coupling the output 72, 74 to their own input 62, 64. Accordingly, the output 72 of the first STE 34 can be coupled to neither, one, or both of the input 62 of the first STE 34 and the input 64 of the second STE 36. Additionally, as each of the inputs 62, 64 may be coupled to a plurality of row routing lines, an OR gate may be utilized to select any of the inputs from these row routing lines along inputs 62, 64, as well as the outputs 72, 74.

In an example, each state transition element 34, 36 comprises a plurality of memory cells 80, such as those often used in dynamic random access memory (DRAM), coupled in parallel to a detect line 82. One such memory cell 80 comprises a memory cell that can be set to a data state, such as one that corresponds to either a high or a low value (e.g., a 1 or 0). The output of the memory cell 80 is coupled to the detect line 82 and the input to the memory cell 80 receives signals based on data on the data stream line 84. In an example, an input at the input block 52 is decoded to select one or more of the memory cells 80. The selected memory cell 80 provides its stored data state as an output onto the detect line 82. For example, the data received at the input block 52 can be provided to a decoder (not shown) and the decoder can select one or more of the data stream lines 84. In an example, the decoder can convert an 8-bit ACSII character to the corresponding 1 of 256 data stream lines 84.

A memory cell 80, therefore, outputs a high signal to the detect line 82 when the memory cell 80 is set to a high value and the data on the data stream line 84 selects the memory cell 80. When the data on the data stream line 84 selects the memory cell 80 and the memory cell 80 is set to a low value, the memory cell 80 outputs a low signal to the detect line 82. The outputs from the memory cells 80 on the detect line 82 are sensed by a detection cell 86.

In an example, the signal on an input line 62, 64 sets the respective detection cell 86 to either an active or inactive state. When set to the inactive state, the detection cell 86 outputs a low signal on the respective output 72, 74 regardless of the signal on the respective detect line 82. When set to an active state, the detection cell 86 outputs a high signal on the respective output line 72, 74 when a high signal is detected from one of the memory cells 80 of the respective STE 34, 36. When in the active state, the detection cell 86 outputs a low signal on the respective output line 72, 74 when the signals from all of the memory cells 82 of the respective STE 34, 36 are low.

In an example, an STE 34, 36 includes 256 memory cells 80 and each memory cell 80 is coupled to a different data stream line 84. Thus, an STE 34, 36 can be programmed to output a high signal when a selected one or more of the data stream lines 84 have a high signal thereon. For example, the STE 34 can have a first memory cell 80 (e.g., bit 0) set high and all other memory cells 80 (e.g., bits 1-255) set low. When the respective detection cell 86 is in the active state, the STE 34 outputs a high signal on the output 72 when the data stream line 84 corresponding to bit 0 has a high signal thereon. In other examples, the STE 34 can be set to output a high signal when one of multiple data stream lines 84 have a high signal thereon by setting the appropriate memory cells 80 to a high value.

In an example, a memory cell 80 can be set to a high or low value by reading bits from an associated register. Accordingly, the STEs 34 can be configured by storing an image created by the compiler 20 into the registers and loading the bits in the registers into associated memory cells 80. In an example, the image created by the compiler 20 includes a binary image of high and low (e.g., 1 and 0) bits. The image can configure the FSM lattice 30 to implement a FSM by cascading the STEs 34, 36. For example, a first STE 34 can be set to an active state by setting the detection cell 86 to the active state. The first STE 34 can be set to output a high signal when the data stream line 84 corresponding to bit 0 has a high signal thereon. The second STE 36 can be initially set to an inactive state, but can be set to, when active, output a high signal when the data stream line 84 corresponding to bit 1 has a high signal thereon. The first STE 34 and the second STE 36 can be cascaded by setting the output 72 of the first STE 34 to couple to the input 64 of the second STE 36. Thus, when a high signal is sensed on the data stream line 84 corresponding to bit 0, the first STE 34 outputs a high signal on the output 72 and sets the detection cell 86 of the second STE 36 to an active state. When a high signal is sensed on the data stream line 84 corresponding to bit 1, the second STE 36 outputs a high signal on the output 74 to activate another STE 36 or for output from the FSM lattice 30.

In an example, a single FSM lattice 30 is implemented on a single physical device, however, in other examples two or more FSM lattices 30 can be implemented on a single physical device (e.g., physical chip). In an example, each FSM lattice 30 can include a distinct data input block 52, a distinct output block 54, a distinct programming interface 56, and a distinct set of configurable elements. Moreover, each set of configurable elements can react (e.g., output a high or low signal) to data at their corresponding data input block 52. For example, a first set of configurable elements corresponding to a first FSM lattice 30 can react to the data at a first data input block 52 corresponding to the first FSM lattice 30. A second set of configurable elements corresponding to a second FSM lattice 30 can react to a second data input block 52 corresponding to the second FSM lattice 30. Accordingly, each FSM lattice 30 includes a set of configurable elements, wherein different sets of configurable elements can react to different input data. Similarly, each FSM lattice 30, and each corresponding set of configurable elements can provide a distinct output. In some examples, an output block 54 from a first FSM lattice 30 can be coupled to an input block 52 of a second FSM lattice 30, such that input data for the second FSM lattice 30 can include the output data from the first FSM lattice 30 in a hierarchical arrangement of a series of FSM lattices 30.

In an example, an image for loading onto the FSM lattice 30 comprises a plurality of bits of data for configuring the configurable elements, the configurable switching elements, and the special purpose elements within the FSM lattice 30. In an example, the image can be loaded onto the FSM lattice 30 to configure the FSM lattice 30 to provide a desired output based on certain inputs. The output block 54 can provide outputs from the FSM lattice 30 based on the reaction of the configurable elements to data at the data input block 52. An output from the output block 54 can include a single bit indicating a search result of a given pattern, a word comprising a plurality of bits indicating search results and non-search results to a plurality of patterns, and a state vector corresponding to the state of all or certain configurable elements at a given moment. As described, a number of FSM lattices 30 may be included in a state machine engine, such as state machine engine 14, to perform data analysis, such as pattern-recognition (e.g., speech recognition, image recognition, etc.) signal processing, imaging, computer vision, cryptography, and others.

FIG. 6 illustrates an example model of a finite state machine (FSM) that can be implemented by the FSM lattice 30. The FSM lattice 30 can be configured (e.g., programmed) as a physical implementation of a FSM. A FSM can be represented as a diagram 90, (e.g., directed graph, undirected graph, pseudograph), which contains one or more root nodes 92. In addition to the root nodes 92, the FSM can be made up of several standard nodes 94 and terminal nodes 96 that are connected to the root nodes 92 and other standard nodes 94 through one or more edges 98. A node 92, 94, 96 corresponds to a state in the FSM. The edges 98 correspond to the transitions between the states.

Each of the nodes 92, 94, 96 can be in either an active or an inactive state. When in the inactive state, a node 92, 94, 96 does not react (e.g., respond) to input data. When in an active state, a node 92, 94, 96 can react to input data. An upstream node 92, 94 can react to the input data by activating a node 94, 96 that is downstream from the node when the input data matches criteria specified by an edge 98 between the upstream node 92, 94 and the downstream node 94, 96. For example, a first node 94 that specifies the character ‘b’ will activate a second node 94 connected to the first node 94 by an edge 98 when the first node 94 is active and the character ‘b’ is received as input data. As used herein, “upstream” refers to a relationship between one or more nodes, where a first node that is upstream of one or more other nodes (or upstream of itself in the case of a loop or feedback configuration) refers to the situation in which the first node can activate the one or more other nodes (or can activate itself in the case of a loop). Similarly, “downstream” refers to a relationship where a first node that is downstream of one or more other nodes (or downstream of itself in the case of a loop) can be activated by the one or more other nodes (or can be activated by itself in the case of a loop). Accordingly, the terms “upstream” and “downstream” are used herein to refer to relationships between one or more nodes, but these terms do not preclude the use of loops or other non-linear paths among the nodes.

In the diagram 90, the root node 92 can be initially activated and can activate downstream nodes 94 when the input data matches an edge 98 from the root node 92. Nodes 94 can activate nodes 96 when the input data matches an edge 98 from the node 94. Nodes 94, 96 throughout the diagram 90 can be activated in this manner as the input data is received. A terminal node 96 corresponds to a search result of a sequence of interest in the input data. Accordingly, activation of a terminal node 96 indicates that a sequence of interest has been received as the input data. In the context of the FSM lattice 30 implementing a pattern recognition function, arriving at a terminal node 96 can indicate that a specific pattern of interest has been detected in the input data.

In an example, each root node 92, standard node 94, and terminal node 96 can correspond to a configurable element in the FSM lattice 30. Each edge 98 can correspond to connections between the configurable elements. Thus, a standard node 94 that transitions to (e.g., has an edge 98 connecting to) another standard node 94 or a terminal node 96 corresponds to a configurable element that transitions to (e.g., provides an output to) another configurable element. In some examples, the root node 92 does not have a corresponding configurable element.

As will be appreciated, although the node 92 is described as a root node and nodes 96 are described as terminal nodes, there may not necessarily be a particular “start” or root node and there may not necessarily be a particular “end” or output node. In other words, any node may be a starting point and any node may provide output.

When the FSM lattice 30 is programmed, each of the configurable elements can also be in either an active or inactive state. A given configurable element, when inactive, does not react to the input data at a corresponding data input block 52. An active configurable element can react to the input data at the data input block 52, and can activate a downstream configurable element when the input data matches the setting of the configurable element. When a configurable element corresponds to a terminal node 96, the configurable element can be coupled to the output block 54 to provide an indication of a search result to an external device.

An image loaded onto the FSM lattice 30 via the programming interface 56 can configure the configurable elements and special purpose elements, as well as the connections between the configurable elements and special purpose elements, such that a desired FSM is implemented through the sequential activation of nodes based on reactions to the data at the data input block 52. In an example, a configurable element remains active for a single data cycle (e.g., a single character, a set of characters, a single clock cycle) and then becomes inactive unless re-activated by an upstream configurable element.

A terminal node 96 can be considered to store a compressed history of past search results. For example, the one or more patterns of input data required to reach a terminal node 96 can be represented by the activation of that terminal node 96. In an example, the output provided by a terminal node 96 is binary, for example, the output indicates whether a search result for a pattern of interest has been generated or not. The ratio of terminal nodes 96 to standard nodes 94 in a diagram 90 may be quite small. In other words, although there may be a high complexity in the FSM, the output of the FSM may be small by comparison.

In an example, the output of the FSM lattice 30 can comprise a state vector. The state vector comprises the state (e.g., activated or not activated) of configurable elements of the FSM lattice 30. In another example, the state vector can include the state of all or a subset of the configurable elements whether or not the configurable elements corresponds to a terminal node 96. In an example, the state vector includes the states for the configurable elements corresponding to terminal nodes 96. Thus, the output can include a collection of the indications provided by all terminal nodes 96 of a diagram 90. The state vector can be represented as a word, where the binary indication provided by each terminal node 96 comprises one bit of the word. This encoding of the terminal nodes 96 can provide an effective indication of the detection state (e.g., whether and what sequences of interest have been detected) for the FSM lattice 30.

As mentioned above, the FSM lattice 30 can be programmed to implement a pattern recognition function. For example, the FSM lattice 30 can be configured to recognize one or more data sequences (e.g., signatures, patterns) in the input data. When a data sequence of interest is recognized by the FSM lattice 30, an indication of that recognition can be provided at the output block 54. In an example, the pattern recognition can recognize a string of symbols (e.g., ASCII characters) to, for example, identify malware or other data in network data.

FIG. 7 illustrates an example of hierarchical structure 100, wherein two levels of FSM lattices 30 are coupled in series and used to analyze data. Specifically, in the illustrated embodiment, the hierarchical structure 100 includes a first FSM lattice 30A and a second FSM lattice 30B arranged in series. Each FSM lattice 30 includes a respective data input block 52 to receive data input, a programming interface block 56 to receive configuring signals and an output block 54.

The first FSM lattice 30A is configured to receive input data, for example, raw data at a data input block. The first FSM lattice 30A reacts to the input data as described above and provides an output at an output block. The output from the first FSM lattice 30A is sent to a data input block of the second FSM lattice 30B. The second FSM lattice 30B can then react based on the output provided by the first FSM lattice 30A and provide a corresponding output signal 102 of the hierarchical structure 100. This hierarchical coupling of two FSM lattices 30A and 30B in series provides a means to provide data regarding past search results in a compressed word from a first FSM lattice 30A to a second FSM lattice 30B. The data provided can effectively be a summary of complex matches (e.g., sequences of interest) that were recorded by the first FSM lattice 30A.

FIG. 7A illustrates a second two-level hierarchy 100 of FSM lattices 30A, 30B, 30C, and 30D, which allows the overall FSM 100 (inclusive of all or some of FSM lattices 30A, 30B, 30C, and 30D) to perform two independent levels of analysis of the input data. The first level (e.g., FSM lattice 30A, FSM lattice 30B, and/or FSM lattice 30C) analyzes the same data stream, which includes data inputs to the overall FSM 100. The outputs of the first level (e.g., FSM lattice 30A, FSM lattice 30B, and/or FSM lattice 30C) become the inputs to the second level, (e.g., FSM lattice 30D). FSM lattice 30D performs further analysis of the combination the analysis already performed by the first level (e.g., FSM lattice 30A, FSM lattice 30B, and/or FSM lattice 30C). By connecting multiple FSM lattices 30A, 30B, and 30C together, increased knowledge about the data stream input may be obtained by FSM lattice 30D.

The first level of the hierarchy (implemented by one or more of FSM lattice 30A, FSM lattice 30B, and FSM lattice 30C) can, for example, perform processing directly on a raw data stream. For example, a raw data stream can be received at an input block 52 of the first level FSM lattices 30A, 30B, and/or 30C and the configurable elements of the first level FSM lattices 30A, 30B, and/or 30C can react to the raw data stream. The second level (implemented by the FSM lattice 30D) of the hierarchy can process the output from the first level. For example, the second level FSM lattice 30D receives the output from an output block 54 of the first level FSM lattices 30A, 30B, and/or 30C at an input block 52 of the second level FSM lattice 30D and the configurable elements of the second level FSM lattice 30D can react to the output of the first level FSM lattices 30A, 30B, and/or 30C. Accordingly, in this example, the second level FSM lattice 30D does not receive the raw data stream as an input, but rather receives the indications of search results for patterns of interest that are generated from the raw data stream as determined by one or more of the first level FSM lattices 30A, 30B, and/or 30C. Thus, the second level FSM lattice 30D can implement a FSM 100 that recognizes patterns in the output data stream from the one or more of the first level FSM lattices 30A, 30B, and/or 30C. However, it should also be appreciated that the second level FSM lattice 30D can additionally receive the raw data stream as an input, for example, in conjunction with the indications of search results for patterns of interest that are generated from the raw data stream as determined by one or more of the first level FSM lattices 30A, 30B, and/or 30C. It should be appreciated that the second level FSM lattice 30D may receive inputs from multiple other FSM lattices in addition to receiving output from the one or more of the first level FSM lattices 30A, 30B, and/or 30C. Likewise, the second level FSM lattice 30D may receive inputs from other devices. The second level FSM lattice 30D may combine these multiple inputs to produce outputs. Finally, while only two levels of FSM lattices 30A, 30B, 30C, and 30D are illustrated, it is envisioned that additional levels of FSM lattices may be stacked such that there are, for example, three, four, 10, 100, or more levels of FSM lattices.

FIG. 8 illustrates an example of a method 110 for a compiler to convert source code into an image used to configure a FSM lattice, such as lattice 30, to implement a FSM. Method 110 includes parsing the source code into a syntax tree (block 112), converting the syntax tree into an automaton (block 114), optimizing the automaton (block 116), converting the automaton into a netlist (block 118), placing the netlist on hardware (block 120), routing the netlist (block 122), and publishing the resulting image (block 124).

In an example, the compiler 20 includes an application programming interface (API) that allows software developers to create images for implementing FSMs on the FSM lattice 30. The compiler 20 provides methods to convert an input set of regular expressions in the source code into an image that is configured to configure the FSM lattice 30. The compiler 20 can be implemented by instructions for a computer having a von Neumann architecture. These instructions can cause a processor 12 on the computer to implement the functions of the compiler 20. For example, the instructions, when executed by the processor 12, can cause the processor 12 to perform actions as described in blocks 112, 114, 116, 118, 120, 122, and 124 on source code that is accessible to the processor 12.

In an example, the source code describes search strings for identifying patterns of symbols within a group of symbols. To describe the search strings, the source code can include a plurality of regular expressions (regexes). A regex can be a string for describing a symbol search pattern. Regexes are widely used in various computer domains, such as programming languages, text editors, network security, and others. In an example, the regular expressions supported by the compiler include criteria for the analysis of unstructured data. Unstructured data can include data that is free form and has no indexing applied to words within the data. Words can include any combination of bytes, printable and non-printable, within the data. In an example, the compiler can support multiple different source code languages for implementing regexes including Perl, (e.g., Perl compatible regular expressions (PCRE)), PHP, Java, and .NET languages.

At block 112 the compiler 20 can parse the source code to form an arrangement of relationally connected operators, where different types of operators correspond to different functions implemented by the source code (e.g., different functions implemented by regexes in the source code). Parsing source code can create a generic representation of the source code. In an example, the generic representation comprises an encoded representation of the regexes in the source code in the form of a tree graph known as a syntax tree. The examples described herein refer to the arrangement as a syntax tree (also known as an “abstract syntax tree”) in other examples, however, a concrete syntax tree as part of the abstract syntax tree, a concrete syntax tree in place of the abstract syntax tree, or other arrangement can be used.

Since, as mentioned above, the compiler 20 can support multiple languages of source code, parsing converts the source code, regardless of the language, into a non-language specific representation, e.g., a syntax tree. Thus, further processing (blocks 114, 116, 118, 120) by the compiler 20 can work from a common input structure regardless of the language of the source code.

As noted above, the syntax tree includes a plurality of operators that are relationally connected. A syntax tree can include multiple different types of operators. For example, different operators can correspond to different functions implemented by the regexes in the source code.

At block 114, the syntax tree is converted into an automaton. An automaton comprises a software model of a FSM which may, for example, comprise a plurality of states. In order to convert the syntax tree into an automaton, the operators and relationships between the operators in the syntax tree are converted into states with transitions between the states. Moreover, in one embodiment, conversion of the automaton is accomplished based on the hardware of the FSM lattice 30.

In an example, input symbols for the automaton include the symbols of the alphabet, the numerals 0-9, and other printable characters. In an example, the input symbols are represented by the byte values 0 through 255 inclusive. In an example, an automaton can be represented as a directed graph where the nodes of the graph correspond to the set of states. In an example, a transition from state p to state q on an input symbol α, i.e. δ(p,α), is shown by a directed connection from node p to node q. In an example, a reversal of an automaton produces a new automaton where each transition p→q on some symbol α is reversed q→p on the same symbol. In a reversal, start states become final states and the final states become start states. In an example, the language recognized (e.g., matched) by an automaton is the set of all possible character strings which when input sequentially into the automaton will reach a final state. Each string in the language recognized by the automaton traces a path from the start state to one or more final states.

At block 116, after the automaton is constructed, the automaton is optimized to reduce its complexity and size, among other things. The automaton can be optimized by combining redundant states.

At block 118, the optimized automaton is converted into a netlist. Converting the automaton into a netlist maps each state of the automaton to a hardware element (e.g., STEs 34, 36, other elements) on the FSM lattice 30, and determines the connections between the hardware elements.

At block 120, the netlist is placed to select a specific hardware element of the target device (e.g., STEs 34, 36, special purpose elements 58) corresponding to each node of the netlist. In an example, placing selects each specific hardware element based on general input and output constraints for the FSM lattice 30.

At block 122, the placed netlist is routed to determine the settings for the configurable switching elements (e.g., inter-block switching elements 40, intra-block switching elements 42, and intra-row switching elements 44) in order to couple the selected hardware elements together to achieve the connections describe by the netlist. In an example, the settings for the configurable switching elements are determined by determining specific conductors of the FSM lattice 30 that will be used to connect the selected hardware elements, and the settings for the configurable switching elements. Routing can take into account more specific limitations of the connections between the hardware elements than can be accounted for via the placement at block 120. Accordingly, routing may adjust the location of some of the hardware elements as determined by the global placement in order to make appropriate connections given the actual limitations of the conductors on the FSM lattice 30.

Once the netlist is placed and routed, the placed and routed netlist can be converted into a plurality of bits for configuring a FSM lattice 30. The plurality of bits are referred to herein as an image (e.g., binary image).

At block 124, an image is published by the compiler 20. The image comprises a plurality of bits for configuring specific hardware elements of the FSM lattice 30. The bits can be loaded onto the FSM lattice 30 to configure the state of STEs 34, 36, the special purpose elements 58, and the configurable switching elements such that the programmed FSM lattice 30 implements a FSM having the functionality described by the source code. Placement (block 120) and routing (block 122) can map specific hardware elements at specific locations in the FSM lattice 30 to specific states in the automaton. Accordingly, the bits in the image can configure the specific hardware elements to implement the desired function(s). In an example, the image can be published by saving the machine code to a computer readable medium. In another example, the image can be published by displaying the image on a display device. In still another example, the image can be published by sending the image to another device, such as a configuring device for loading the image onto the FSM lattice 30. In yet another example, the image can be published by loading the image onto a FSM lattice (e.g., the FSM lattice 30).

In an example, an image can be loaded onto the FSM lattice 30 by either directly loading the bit values from the image to the STEs 34, 36 and other hardware elements or by loading the image into one or more registers and then writing the bit values from the registers to the STEs 34, 36 and other hardware elements. In an example, the hardware elements (e.g., STEs 34, 36, special purpose elements 58, configurable switching elements 40, 42, 44) of the FSM lattice 30 are memory mapped such that a configuring device and/or computer can load the image onto the FSM lattice 30 by writing the image to one or more memory addresses.

Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, the code may be tangibly stored on one or more volatile or non-volatile computer-readable media during execution or at other times. These computer-readable media may include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

Referring now to FIG. 9, an embodiment of the state machine engine 14 (e.g., a single device on a single chip) is illustrated. As previously described, the state machine engine 14 is configured to receive data from a source, such as the memory 16 over a data bus. In the illustrated embodiment, data may be sent to the state machine engine 14 through a bus interface, such as a double data rate three (DDR3) bus interface 130. The DDR3 bus interface 130 may be capable of exchanging (e.g., providing and receiving) data at a rate greater than or equal to 1 GByte/sec. Such a data exchange rate may be greater than a rate that data is analyzed by the state machine engine 14. As will be appreciated, depending on the source of the data to be analyzed, the bus interface 130 may be any suitable bus interface for exchanging data to and from a data source to the state machine engine 14, such as a NAND Flash interface, peripheral component interconnect (PCI) interface, gigabit media independent interface (GMMI), etc. As previously described, the state machine engine 14 includes one or more FSM lattices 30 configured to analyze data. Each FSM lattice 30 may be divided into two half-lattices. In the illustrated embodiment, each half lattice may include 24K STEs (e.g., STEs 34, 36), such that the lattice 30 includes 48K STEs. The lattice 30 may comprise any desirable number of STEs, arranged as previously described with regard to FIGS. 2-5. Further, while only one FSM lattice 30 is illustrated, the state machine engine 14 may include multiple FSM lattices 30, as previously described.

Data to be analyzed may be received at the bus interface 130 and provided to the FSM lattice 30 through a number of buffers and buffer interfaces. In the illustrated embodiment, the data path includes input buffers 132, an instruction buffer 133, process buffers 134, and an inter-rank (IR) bus and process buffer interface 136. The input buffers 132 are configured to receive and temporarily store data to be analyzed. In one embodiment, there are two input buffers 132 (input buffer A and input buffer B). Data may be stored in one of the two data input 132, while data is being emptied from the other input buffer 132, for analysis by the FSM lattice 30. The bus interface 130 may be configured to provide data to be analyzed to the input buffers 132 until the input buffers 132 are full. After the input buffers 132 are full, the bus interface 130 may be configured to be free to be used for other purpose (e.g., to provide other data from a data stream until the input buffers 132 are available to receive additional data to be analyzed). In the illustrated embodiment, the input buffers 132 may be 32 KBytes each. The instruction buffer 133 is configured to receive instructions from the processor 12 via the bus interface 130, such as instructions that correspond to the data to be analyzed and instructions that correspond to configuring the state machine engine 14. The IR bus and process buffer interface 136 may facilitate providing data to the process buffer 134. The IR bus and process buffer interface 136 can be used to ensure that data is processed by the FSM lattice 30 in order. The IR bus and process buffer interface 136 may coordinate the exchange of data, timing data, packing instructions, etc. such that data is received and analyzed correctly. Generally, the IR bus and process buffer interface 136 allows the analyzing of multiple data sets in parallel through a logical rank of FSM lattices 30. For example, multiple physical devices (e.g., state machine engines 14, chips, separate devices) may be arranged in a rank and may provide data to each other via the IR bus and process buffer interface 136. For purposes of this application the term “rank” refers to a set of state machine engines 14 connected to the same chip select. In the illustrated embodiment, the IR bus and process buffer interface 136 may include a 32 bit data bus. In other embodiments, the IR bus and process buffer interface 136 may include any suitable data bus, such as a 128 bit data bus.

In the illustrated embodiment, the state machine engine 14 also includes a de-compressor 138 and a compressor 140 to aid in providing state vector data through the state machine engine 14. The compressor 140 and de-compressor 138 work in conjunction such that the state vector data can be compressed to minimize the data providing times. By compressing the state vector data, the bus utilization time may be minimized. The compressor 140 and de-compressor 138 can also be configured to handle state vector data of varying burst lengths. By padding compressed state vector data and including an indicator as to when each compressed region ends, the compressor 140 may improve the overall processing speed through the state machine engine 14. The compressor 140 may be used to compress results data after analysis by the FSM lattice 30. The compressor 140 and de-compressor 138 may also be used to compress and decompress configuration data. In one embodiment, the compressor 140 and de-compressor 138 may be disabled (e.g., turned off) such that data flowing to and/or from the compressor 140 and de-compressor 138 is not modified.

As previously described, an output of the FSM lattice 30 can comprise a state vector. The state vector comprises the state (e.g., activated or not activated) of the STEs 34, 36 of the FSM lattice 30 and the dynamic (e.g., current) count of the counter 58. The state machine engine 14 includes a state vector system 141 having a state vector cache memory 142, a state vector memory buffer 144, a state vector intermediate input buffer 146, and a state vector intermediate output buffer 148. The state vector system 141 may be used to store multiple state vectors of the FSM lattice 30 and to provide a state vector to the FSM lattice 30 to restore the FSM lattice 30 to a state corresponding to the provided state vector. For example, each state vector may be temporarily stored in the state vector cache memory 142. For example, the state of each STE 34, 36 may be stored, such that the state may be restored and used in further analysis at a later time, while freeing the STEs 34, 36 for further analysis of a new data set (e.g., search terms). Like a typical cache, the state vector cache memory 142 allows storage of state vectors for quick retrieval and use, here by the FSM lattice 30, for instance. In the illustrated embodiment, the state vector cache memory 142 may store up to 512 state vectors.

As will be appreciated, the state vector data may be exchanged between different state machine engines 14 (e.g., chips) in a rank. The state vector data may be exchanged between the different state machine engines 14 for various purposes such as: to synchronize the state of the STEs 34, 36 of the FSM lattices 30 of the state machine engines 14, to perform the same functions across multiple state machine engines 14, to reproduce results across multiple state machine engines 14, to cascade results across multiple state machine engines 14, to store a history of states of the STEs 34, 36 used to analyze data that is cascaded through multiple state machine engines 14, and so forth. Furthermore, it should be noted that within a state machine engine 14, the state vector data may be used to quickly configure the STEs 34, 36 of the FSM lattice 30. For example, the state vector data may be used to restore the state of the STEs 34, 36 to an initialized state (e.g., to prepare for a new input data set), or to restore the state of the STEs 34, 36 to prior state (e.g., to continue searching of an interrupted or “split” input data set). In certain embodiments, the state vector data may be provided to the bus interface 130 so that the state vector data may be provided to the processor 12 (e.g., for analysis of the state vector data, reconfiguring the state vector data to apply modifications, reconfiguring the state vector data to improve efficiency of the STEs 34, 36, and so forth).

For example, in certain embodiments, the state machine engine 14 may provide cached state vector data (e.g., data stored by the state vector system 141) from the FSM lattice 30 to an external device. The external device may receive the state vector data, modify the state vector data, and provide the modified state vector data to the state machine engine 14 for configuring the FSM lattice 30. Accordingly, the external device may modify the state vector data so that the state machine engine 14 may skip states (e.g., jump around) as desired.

The state vector cache memory 142 may receive state vector data from any suitable device. For example, the state vector cache memory 142 may receive a state vector from the FSM lattice 30, another FSM lattice 30 (e.g., via the IR bus and process buffer interface 136), the de-compressor 138, and so forth. In the illustrated embodiment, the state vector cache memory 142 may receive state vectors from other devices via the state vector memory buffer 144. Furthermore, the state vector cache memory 142 may provide state vector data to any suitable device. For example, the state vector cache memory 142 may provide state vector data to the state vector memory buffer 144, the state vector intermediate input buffer 146, and the state vector intermediate output buffer 148.

Additional buffers, such as the state vector memory buffer 144, state vector intermediate input buffer 146, and state vector intermediate output buffer 148, may be utilized in conjunction with the state vector cache memory 142 to accommodate rapid retrieval and storage of state vectors, while processing separate data sets with interleaved packets through the state machine engine 14. In the illustrated embodiment, each of the state vector memory buffer 144, the state vector intermediate input buffer 146, and the state vector intermediate output buffer 148 may be configured to temporarily store one state vector. The state vector memory buffer 144 may be used to receive state vector data from any suitable device and to provide state vector data to any suitable device. For example, the state vector memory buffer 144 may be used to receive a state vector from the FSM lattice 30, another FSM lattice 30 (e.g., via the IR bus and process buffer interface 136), the de-compressor 138, and the state vector cache memory 142. As another example, the state vector memory buffer 144 may be used to provide state vector data to the IR bus and process buffer interface 136 (e.g., for other FSM lattices 30), the compressor 140, and the state vector cache memory 142.

Likewise, the state vector intermediate input buffer 146 may be used to receive state vector data from any suitable device and to provide state vector data to any suitable device. For example, the state vector intermediate input buffer 146 may be used to receive a state vector from an FSM lattice 30 (e.g., via the IR bus and process buffer interface 136), the de-compressor 138, and the state vector cache memory 142. As another example, the state vector intermediate input buffer 146 may be used to provide a state vector to the FSM lattice 30. Furthermore, the state vector intermediate output buffer 148 may be used to receive a state vector from any suitable device and to provide a state vector to any suitable device. For example, the state vector intermediate output buffer 148 may be used to receive a state vector from the FSM lattice 30 and the state vector cache memory 142. As another example, the state vector intermediate output buffer 148 may be used to provide a state vector to an FSM lattice 30 (e.g., via the IR bus and process buffer interface 136) and the compressor 140.

Once a result of interest is produced by the FSM lattice 30, an event vector may be stored in an event vector memory 150, whereby, for example, the event vector indicates at least one search result (e.g., detection of a pattern of interest). The event vector can then be sent to an event buffer 152 for transmission over the bus interface 130 to the processor 12, for example. As previously described, the results may be compressed. The event vector memory 150 may include two memory elements, memory element A and memory element B, each of which contains the results obtained by processing the input data in the corresponding input buffers 132 (e.g., input buffer A and input buffer B). In one embodiment, each of the memory elements may be DRAM memory elements or any other suitable storage devices. In some embodiments, the memory elements may operate as initial buffers to buffer the event vectors received from the FSM lattice 30, along results bus 151. For example, memory element A may receive event vectors, generated by processing the input data from input buffer A, along results bus 151 from the FSM lattice 30. Similarly, memory element B may receive event vectors, generated by processing the input data from input buffer B, along results bus 151 from the FSM lattice 30.

In one embodiment, the event vectors provided to the results memory 150 may indicate that a final result has been found by the FSM lattice 30. For example, the event vectors may indicate that an entire pattern has been detected. Alternatively, the event vectors provided to the results memory 150 may indicate, for example, that a particular state of the FSM lattice 30 has been reached. For example, the event vectors provided to the results memory 150 may indicate that one state (i.e., one portion of a pattern search) has been reached, so that a next state may be initiated. In this way, the event vector 150 may store a variety of types of results.

In some embodiments, IR bus and process buffer interface 136 may provide data to multiple FSM lattices 30 for analysis. This data may be time multiplexed. For example, if there are eight FSM lattices 30, data for each of the eight FSM lattices 30 may be provided to all of eight IR bus and process buffer interfaces 136 that correspond to the eight FSM lattices 30. Each of the eight IR bus and process buffer interfaces 136 may receive an entire data set to be analyzed. Each of the eight IR bus and process buffer interfaces 136 may then select portions of the entire data set relevant to the FSM lattice 30 associated with the respective IR bus and process buffer interface 136. This relevant data for each of the eight FSM lattices 30 may then be provided from the respective IR bus and process buffer interfaces 136 to the respective FSM lattice 30 associated therewith.

The event vector 150 may operate to correlate each received result with a data input that generated the result. To accomplish this, a respective result indicator may be stored corresponding to, and in some embodiments, in conjunction with, each event vector received from the results bus 151. In one embodiment, the result indicators may be a single bit flag. In another embodiment, the result indicators may be a multiple bit flag. If the result indicators may include a multiple bit flag, the bit positions of the flag may indicate, for example, a count of the position of the input data stream that corresponds to the event vector, the lattice that the event vectors correspond to, a position in set of event vectors, or other identifying information. These results indicators may include one or more bits that identify each particular event vector and allow for proper grouping and transmission of event vectors, for example, to compressor 140. Moreover, the ability to identify particular event vectors by their respective results indicators may allow for selective output of desired event vectors from the event vector memory 150. For example, only particular event vectors generated by the FSM lattice 30 may be selectively latched as an output. These result indicators may allow for proper grouping and provision of results, for example, to compressor 140. Moreover, the ability to identify particular event vectors by their respective result indicators allow for selective output of desired event vectors from the result memory 150. Thus, only particular event vectors provided by the FSM lattice 30 may be selectively provided to compressor 140.

Additional registers and buffers may be provided in the state machine engine 14, as well. In one embodiment, for example, a buffer may store information related to more than one process whereas a register may store information related to a single process. For instance, the state machine engine 14 may include control and status registers 154. In addition, a program buffer system (e.g., restore buffers 156) may be provided for initializing the FSM lattice 30. For example, initial (e.g., starting) state vector data may be provided from the program buffer system to the FSM lattice 30 (e.g., via the de-compressor 138). The de-compressor 138 may be used to decompress configuration data (e.g., state vector data, routing switch data, STE 34, 36 states, Boolean function data, counter data, match MUX data) provided to program the FSM lattice 30.

Similarly, a repair map buffer system (e.g., save buffers 158) may also be provided for storage of data (e.g., save maps) for setup and usage. The data stored by the repair map buffer system may include data that corresponds to repaired hardware elements, such as data identifying which STEs 34, 36 were repaired. The repair map buffer system may receive data via any suitable manner. For example, data may be provided from a “fuse map” memory, which provides the mapping of repairs done on a device during final manufacturing testing, to the save buffers 158. As another example, the repair map buffer system may include data used to modify (e.g., customize) a standard programming file so that the standard programming file may operate in a FSM lattice 30 with a repaired architecture (e.g., bad STEs 34, 36 in a FSM lattice 30 may be bypassed so they are not used). The compressor 140 may be used to compress data provided to the save buffers 158 from the fuse map memory. As illustrated, the bus interface 130 may be used to provide data to the restore buffers 156 and to provide data from the save buffers 158. As will be appreciated, the data provided to the restore buffers 156 and/or provided from the save buffers 158 may be compressed. In some embodiments, data is provided to the bus interface 130 and/or received from the bus interface 130 via a device external to the state machine engine 14 (e.g., the processor 12, the memory 16, the compiler 20, and so forth). The device external to the state machine engine 14 may be configured to receive data provided from the save buffers 158, to store the data, to analyze the data, to modify the data, and/or to provide new or modified data to the restore buffers 156.

The state machine engine 14 includes a lattice programming and instruction control system 159 used to configure (e.g., program) the FSM lattice 30 as well as provide inserted instructions, as will be described in greater detail below. As illustrated, the lattice programming and instruction control system 159 may receive data (e.g., configuration instructions) from the instruction buffer 133. Furthermore, the lattice programming and instruction control system 159 may receive data (e.g., configuration data) from the restore buffers 156. The lattice programming and instruction control system 159 may use the configuration instructions and the configuration data to configure the FSM lattice 30 (e.g., to configure routing switches, STEs 34, 36, Boolean cells, counters, match MUX) and may use the inserted instructions to correct errors during the operation of the state machine engine 14. The lattice programming and instruction control system 159 may also use the de-compressor 138 to de-compress data and the compressor 140 to compress data (e.g., for data exchanged with the restore buffers 156 and the save buffers 158).

As previously described, one or more state machine engines 14 may be in communication with the processor 12 via the integrated circuit device 13. The integrated circuit device 13 may function as a controller that translates between one or more interfaces (e.g., PCIe) used by a motherboard on which the processor 12 is disposed and one or more different interfaces (e.g., DDR) used by chips on which the state machine engines 14 are disposed. According to some embodiments of the present disclosure, unused resources of the integrated circuit device 13 may be programmed as custom compute cores 17 that perform various functions. In some embodiments, certain functions may be included in the custom compute cores 17 that otherwise may be performed by the processor 12. In this way, the processor 12 may be freed to perform other functions, which may enhance the processing throughput performance of the processor 12.

To clarify, FIG. 10 illustrates an example of a method 200 for the integrated circuit device 13 to receive (block 202) and implement (block 204) one or more custom compute cores, according to various embodiments. As previously described, the one or more custom compute cores 17 may each include a preprocessing core or a post-processing core. The preprocessing cores may include instructions that, when executed by the integrated circuit device 13, perform certain functionality on the input data prior to sending the input data to the state machine engine 14. Each of the one or more preprocessing cores may be dedicated to performing a specific functionality or each of the one or more preprocessing cores may perform several different functionalities. Moreover, it should be understood that processing may be distributed between the preprocessing cores such that subsets of an overarching functionality are performed by individual preprocessing cores to enhance processing speeds.

In some embodiments, the architecture of the state machine engine 14 may specify that input data be formatted as a particular data structure to be validly recognized and processed. The input data may include a raw data stream of input symbols (e.g., the alphabet, numerals (0-9), etc.) from a database or data source to be searched. As such, in some embodiments, the preprocessing functionality may include organizing the input data to match a particular data structure as expected by the state machine engine 14. That is, after this preprocessing functionality executes, the reorganized input data may map directly to the programmed state machine engine 14. The design of the state machine engine 14 may be such that a tight coupling is achieved where expected input data is preprocessed to match the architecture in the state machine engine 14. The particular data structure may be provided in a specification (e.g., application programming interface specification) or description of acceptable input data.

In addition, the preprocessing functionality may include compressing the input data to enable faster transmission speed. The input data may be complex and/or large in size, which may lead to processing throughput performance delays. For example, the input data may include an entire database of symbols to search for particular patterns and/or matches. Thus, to increase the processing throughput performance, the preprocessing cores may compress the input data prior to submitting the input data to the state machine engine 14. Further, the preprocessing functionality may include sorting the input data, merging the input data, deleting certain data in the input data, modifying (e.g., inserting, changing) data into the input data, segmenting the input data, filtering the input data, or the like. As may be appreciated, any suitable data preprocessing functionality may be included in the preprocessing cores and programmed into the open space of the integrated circuit device 13.

In some embodiments, post-processing cores may include instructions that, when executed by the integrated circuit device 13, perform certain functionality on the output data prior to sending the output data to the processor 12. Each of the one or more post-processing cores may be dedicated to performing a specific functionality or each of the one or more post-processing cores may perform several different functionalities. Moreover, it should be understood that processing may be distributed between the post-processing cores such that subsets of an overarching functionality may be performed by individual post-processing cores to enhance processing speed.

It should be understood that the data output from the state machine engine 14 may include a format specific to the programmed state machine engine 14. The output data may include one or more event vectors that may indicate the results (e.g., matches, non-matches, etc.) of the search performed by the state machine engine 14, the input data searched, or the like. As a result, the output data may be complex and/or large, which may negatively impact processing throughput performance. Thus, in some embodiments, the functionality of the post-processing cores may include compressing the output data to reduce its size. For example, the post-processing cores may compress the event vectors included in the output data to minimize the amount of traffic in the dataflow on a bus of the integrated circuit device 13. Compressing the output data using the post-processing core may result in enhanced processing throughput performance of the processor 12 and the state machine engine 14 because the enhanced data transfer speed may enable more data to be processed at a faster rate.

Additionally or alternatively, the post-processing core may also include instructions that, when executed by the integrated circuit device 13, perform other data processing functionality, such as data merging, sorting, segmenting, deleting, inserting, filtering or the like on the output data. Indeed, it should be understood that any suitable post-processing functionality may be implemented in the unused resources of the integrated circuit device 13 as post-processing cores.

In some embodiments, the custom compute cores 17 may be implemented using a software development kit (SDK). The SDK may include libraries and/or definitions of application programming interfaces (APIs) that are referenceable by the custom compute core instructions. For example, a specification of the APIs may define how the custom function of the custom compute cores 17 can interface to the existing base build and pipeline of the integrated circuit device 13. In some embodiments, the specification defines the usable address map and physical periphery interface for direct memory access (DMA) transactions and data management. Thus, the SDK enables the user to develop their custom compute cores 17 and implement the custom compute cores 17 into the fabric resource of the integrated circuit device 13 such that the input data and output data flows through the custom compute cores 17 as desired. As described above, the custom compute cores 17 may be defined as RTL or OpenCL descriptions to target the unused programmable space of the integrated circuit device 13. Additionally or alternatively, it should be noted that the custom compute cores 17 may be reprogrammable in that the user may modify the instruction set using the software development kit (SDK). Thus, the functionality of the custom compute cores 17 may be modified if desired by the user.

After the custom compute cores 17 are implemented into the fabric of the integrated circuit device 13, the method 200 may also include the integrated circuit device 13 executing (block 206) the one or more preprocessing cores when input data is received from a host application executed by the processor 12. Also, the method 200 may include the integrated circuit device 13 executing (block 208) the one or more post-processing cores when output data is received from the state machine engine 14. To help illustrate the flow of data through the custom compute cores 17 (e.g., preprocessing cores and/or post-processing cores) of the integrated circuit device 13, FIG. 11 depicts an example integrated circuit device 13.

In particular, FIG. 11 illustrates an example integrated circuit device 13 interfacing between the processor 12 and the state machine engine 14, according to various embodiments. As depicted, the integrated circuit device 13 and the state machine engine 14 are depicted as being disposed on the hardware accelerator 15 (e.g., peripheral component interconnect express (PCIe) accelerator card). Also, the processor 12 is depicted as external to the hardware accelerator 15 and in communication with the state machine engine 14 via the integrated circuit device 13. As described above, in some embodiments, the integrated circuit device 13 may be a field programmable gate array (FPGA) controller. However, it should be understood that any suitable programmable circuit that integrates with the hardware accelerator 15 may be used as the interface between the processor 12 and the state machine engine 14 and as the controller of the state machine engine 14.

As depicted, the components of the integrated circuit device 13 include first interface circuitry 210, a direct memory access (DMA) engine 212, second interface circuitry 214, and custom compute cores 17. The custom compute cores 17 may include one or more preprocessing cores 218 and one or more post-processing cores 220. The processor 12 is connected to the integrated circuit device 13, and the integrated circuit device 13 is connected to the state machine engine 14. More specifically, a card edge connector (PCIe) of the processor 12 may be connected to the first interface circuitry 210 of the integrated circuit device 13. The first interface circuitry 210 may include PCIe circuitry components or the like. The first interface circuitry 210 is connected to the DMA engine 212, and the DMA engine 212 is further connected to the second interface circuitry 214. The second interface circuitry 214 may include a DRAM device controller that provides high-performance controller interfaces to industry-standard DDR memory. As such, the second interface circuitry 214 is connected to the state machine engine 14, which may be included on a DRAM memory chip. Thus, the data that flows through the integrated circuit device 13 is translated from the PCIe interface of the first interface circuitry 210 used by the processor 12 to the DDR interface of the second interface circuitry 214 used by the state machine engine 14. Further, the DMA engine 212 controls whether the data is written to or read from the state machine engine 14 and sends the data through the custom compute cores 17 as desired.

In instances where the custom compute cores 17 are not present in the integrated circuit device 13 fabric resources, data transactions are queued through the DMA engine 212 via the host application executed by the processor 12 and a device driver API of the integrated circuit device 13. When it is desired to write data to the state machine engine 14, a DMA write from the memory 16 used by the processor 12 to the state machine engine 14 is queued. The input data (e.g., raw data stream of symbols to be searched) may be written directly to the state machine engine 14 without preprocessing, other than the translation from the PCIe interface to the DDR interface. Likewise, when it is desired to read data from the state machine engine 14, a DMA read from the state machine engine 14 is queued. The output data may be read from the state machine engine 14 directly to the processor 12 without post-processing, other than the translation from the DDR interface to the PCIe interface.

However, in some embodiments of the present disclosure, the implementation of the custom compute cores 17 into the unused resources of the integrated circuit device 13 modifies the flow of data. For example, when it is desired to write input data to the state machine engine 14, the DMA engine 212 inputs the input data into the one or more preprocessing cores 218 to perform their custom functions. For example, one of the preprocessing cores 218 may organize the input data to match a specific format depending on the registers of the chip implementing the state machine engine 14. After preprocessing is complete, the DMA engine 212 sends the preprocessed input data to the second interface circuitry 214 (e.g., DDR interface) to be translated for the state machine engine 14 to consume. This extra step in the data flow of preprocessing the input data using the preprocessing cores 218 may be coordinated through the integrated circuit device driver's API.

Likewise, when it is desired to read output data from the state machine engine 14 and the one or more post-processing cores 220 are implemented in the resources of the integrated circuit device 13, the DMA engine 212 inputs the output data from the state machine engine 14 into the one or more post-processing cores 220 to perform their custom functions. For example, one of the post-processing cores 220 may compress the output data (e.g., event vectors). After post-processing is complete, the DMA engine 212 sends the post-processed output data to the first interface circuitry 210 (e.g., PCIe interface) to be translated for the processor 12 to consume. This extra step in the data flow of post-processing the output data using the post-processing cores 220 may be coordinated through the integrated circuit device driver's API.

A more specific diagram of example components of the integrated circuit device 13 is depicted in FIG. 12, according to various embodiments. As depicted, a clock and reset logic 222 is connected to a bus 224 to provide a clock signal to drive and reset various components connected to the bus 222. The bus 222 may enable data and signals to be transmitted between the various components connected thereto. As depicted, the components connected to the bus 224 include the first interface circuitry 210, the DMA engine 212, the second interface circuitry 214, and the custom compute cores 17 (e.g., one or more preprocessing cores 218 and/or post-processing cores 220). There are four example second interface circuitries 214 depicted because four automata chips, each including one state machine engine 14, are connected to the displayed interface circuit device 13. Thus, there is one second interface circuitry 214 disposed on each edge of the integrated circuit device 13 for each of the four state machine engines 14. It should be understood that any suitable number of second interface circuitries 214 may be disposed on the integrated circuit device 13 depending on the number of state machine engines 14 in an array disposed on the hardware accelerator 15. Additional components connected to the bus 224 may include a system identification component 224, a random access memory (RAM) 226 for the processor 12, a RAM 230 for DMA descriptors, parallel input/output (GP PIO) 232, phase-locked loop/delay-locked loop logic 234, and reconfiguration circuitry 236 for the first interface circuitry 210.

In some embodiments, as depicted, the first interface circuitry 210 (e.g., native physical layer PCIe circuitry) may include several components, such as a physical layer media access control (PHYMAC) component, a clock 240, a data link layer 242, a transaction layer 244, and an adapter 246. The PHYMAC 238, the clock 240, the data link layer 242, the transaction layer 244, and the adapter 246 may be serially connected. The reconfiguration circuitry 236 may be connected to the transaction layer 244. Also, the PHYMAC 238 may be connected to transceiver circuitry 248 to enable communication and data transmission with the processor 12. The adapter 246 may translate output data received from the post-processing cores 220 to the PCIe interface prior to the transceiver block 248 transmitting the translated output data to the processor 12.

In some embodiments, as depicted, the DMA engine 212 may include several components, such as control and status circuitry 250, descriptor processor 252, and DMA write and read circuitry 254. It should be understood that the DMA engine 212 may be used to queue read and write data transactions between the processor 12 and the state machine engines 14.

In some embodiments, as depicted, the second interface circuitry 214 (e.g., DDR interface circuitry) may include several components, such as control logic circuitry 256 and a data path module 258. The control logic circuitry 256 may perform translation of the input data received from the DMA engine 212 after preprocessing by the preprocessing cores 218 to meet the DDR interface. The control logic circuitry 256 and the data path module 258 are each connected to respective double data rate input/out (DDIO) and resynch logic circuitry 260 and 262. The DDIO and resynch logic circuitries 260 and 262 may be further coupled to the DDR3 bus interface 130 of the state machine engine 14. As such, the DDIO and resynch logic circuitries 260 and 262 may send the input data to the state machine engine 14 and receive the output data from the state machine engine 14. The DDIO and the resynch logic circuitries 260 and 262 may be digital components that double or halve the data rate of a communication channel.

In some embodiments, when input data (e.g., data stream of symbols) is received at the transceiver block 248 from edge connectors (e.g., PCIe) of the motherboard on which the processor 12 is included, the DMA engine 212 may route the input data into the one or more preprocessing cores 218 via the bus 224 prior to sending the input data to the state machine engine 14. The one or more preprocessing cores 218 may perform their respective custom function on the input data and send the preprocessed input data via the bus 224 to the DMA engine 212. The DMA engine 212 may then write the preprocessed input data to the state machine engine 14 via the second interface circuitry 256, which translates the preprocessed input data to the DDR interface used by the chip on which the state machine engine 14 is disposed.

Likewise, when output data (e.g., data vectors) is received at the DDIO and resynch logic circuitries 262 from the state machine engine 14, the DMA engine 212 may route the output data into the one or more post-processing cores 220 via the bus 224 prior to sending the output data to the processor 12. The one or more post-processing cores 218 may perform their respective custom function on the output data and send the post-processed output data via the bus 224 to the DMA engine 212. The DMA engine 212 may then read the post-processed output data to the processor 12 via the first interface circuitry 256, which translates the post-processed output data to the PCIe interface used by the edge connector of the motherboard on which the processor 12 is disposed.

The data flow between the processor 12 running a host application, the integrated circuit device 13 using the custom compute cores 17, and the state machine engine 14 is further described below with regard to FIGS. 13 and 14. Starting with FIG. 13, an example of a method 270 for the integrated circuit device 13 to perform functionality provided by the custom compute cores 17 during runtime is depicted. The method 270 may be performed by the integrated circuit device 13 (e.g., FPGA) that may function as a controller and interface translator for the state machine engine 14. The method 270 may include receiving (block 272) the input data to be processed from the edge connectors of the motherboard including the processor 12 that runs the host application.

As previously described, the input data may include a raw data stream of symbols (e.g., the alphabet, numerals (0-9)) to be searched for patterns or certain matches. The input data may be rather complex and/or large in size. As such, the method 270 may include performing (block 274) one or more preprocessing functions using the one or more preprocessing cores 218. As discussed above, this step 274 may include the DMA engine 212 sending the input data received from the first interface circuitry 210 to the preprocessing cores 218 via the bus 224 prior to writing the input data to the state machine engine 14. The preprocessing functions may include data organization, compression, serialization, segmentation, merging, deletion, insertion, or the like.

After the input data is preprocessed by the preprocessing cores 218, the preprocessed input data may be output (block 276) to the state machine engine 14. This step 276 may include the preprocessed input data being sent to the DMA engine 212 via the bus 224. The DMA engine 212 may receive the preprocessed input data and write it to the state machine engine 14 by sending the preprocessed data to the second interface circuitry 214 via the bus 224. The second interface circuitry 214 may translate the preprocessed input data to the DDR interface used by the chip on which the state machine engine 14 is disposed. The state machine engine 14 may process the preprocessed input data (e.g., perform pattern recognition) and output the results in the form of data vectors (e.g., event vectors).

As such, the integrated circuit device 13 may receive (block 278) the output data from the state machine engine 14. In particular, the second interface circuitry 214 may receive the output data and send the output data to the DMA engine 212 via the bus 224. The DMA engine 212 may send the output data to the one or more post-processing cores 220. The method 270 may also include performing (block 280) one or more post-processing functions on the output data using the one or more post-processing cores 220. The post-processing functions may include data organization, compression, serialization, segmentation, merging, deletion, insertion, or the like. The post-processed output data may be sent back to the DMA engine 212. The DMA engine 212 may then output (block 282) the post-processed output data to the processor 12 by sending the post-processed output data to the first interface circuitry 210, which translates the post-processed output data to the PCIe interface used by the processor 12.

Another example of a method 290 for processing data using the disclosed embodiments is depicted in FIG. 14. FIG. 14 illustrates example steps performed by the processor 12 and the integrated circuit device 13 to cooperatively process data, according to various embodiments. The method 290 begins with the processor 12 executing a host application to compile (block 292) input data (e.g., raw data stream of symbols) to be searched. The input data may be sent to the integrated circuit device 13. The input data may be sent to the one or more preprocessing cores 218 by the DMA engine 212 for preprocessing (block 294). After the input data is preprocessed, the DMA engine 212 may send (block 296) the preprocessed input data to the automata input buffers (e.g., second interface circuitry 214).

The second interface circuitry 214 may translate the preprocessed input data to the DDR interface for processing by the state machine engine 14. The state machine engine 14 may output the event vectors that result from the searching, and the integrated circuit device 13 may receive (block 298) the event vectors. The method 290 may include the DMA engine 212 outputting (block 300) the event vectors to the processor 12 by sending the event vectors to the bus 224. The method 290 may also include post-processing (block 302) the event vectors using the post-processing cores 220 prior to sending the event vectors to the processor 12 via the first interface circuitry 210. Once the first interface circuitry 210 translates the post-processed event vectors to the PCIe interface, the translated, post-processed event vectors are sent to the processor 12. The processor 12 may interpret (block 304) the results included in the received event vectors. As may be appreciated, using the custom compute cores 17 in the unused resources of the integrated circuit 13 may free the processor 12 to perform other functions and may enhance processing throughput performance of data input to and output from the state machine engine 14.

While the techniques may be susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and have been described in detail herein. However, it should be understood that the present disclosure is not intended to be limited to the particular forms disclosed. Rather, the disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure as defined by the following appended claims. 

What is claimed is:
 1. A system, comprising: a processor configured to initiate an analysis of data; and a hardware accelerator coupled to the processor, wherein the hardware accelerator comprises: a state machine engine comprising a plurality of configurable circuits, wherein each configurable circuit of the plurality of configurable circuits comprises a data analysis circuit comprising a plurality of memory components programmed with configuration data to selectively set a data state of each memory component of the plurality of memory components as one of a high state and a low state in conjunction with a pattern to be searched as the analysis of data, wherein the data analysis circuit is configured to analyze at least a portion of the data based on the data state of each memory component as selected by a respective data signal of the portion of the data; and an integrated circuit device comprising: a direct memory access (DMA) engine configured to write the data to the state machine engine and read output data from the state machine engine; one or more preprocessing cores configured to perform one or more custom preprocessing functions on the data prior to the DMA engine writing the data to the state machine engine; and one or more post-processing cores configured to perform one or more custom post-processing functions on the output data subsequent to the DMA engine reading the output data from the state machine engine.
 2. The system of claim 1, wherein at least one of the one or more custom post-processing functions comprises compressing the output data to reduce an amount of traffic on a bus of the integrated circuit device and improve processing throughput performance.
 3. The system of claim 1, wherein at least one of the one or more preprocessing functions comprises organizing the data to match an architecture of the state machine engine.
 4. The system of claim 1, wherein the one or more preprocessing cores and the one or more post-processing cores are instruction sets implemented in unused fabric resources of the integrated circuit device as firmware.
 5. The system of claim 4, wherein the instruction sets are defined as register transfer level (RTL) or open computing language (OpenCL) descriptions.
 6. The system of claim 4, wherein the instruction sets implement a specification that defines a usable address map and a physical periphery interface of the integrated circuit device to interact with the DMA engine and a driver of the integrated circuit device.
 7. The system of claim 1, wherein the one or more preprocessing functions, the one or more custom post-processing functions, or some combination thereof are not performed by the processor to enable the processor to increase processing throughput performance.
 8. The system of claim 1, wherein the one or more preprocessing functions comprise at least one of data compression, organization, segmentation, insertion, deletion, modification, merging, filtering, or some combination thereof.
 9. The system of claim 1, wherein the one or more custom post-processing functions comprise at least one of data compression, organization, segmentation, insertion, deletion, modification, merging, filtering, or some combination thereof.
 10. The system of claim 1, wherein the integrated circuit device comprises a first type of interface coupled to the processor and a second type of interface coupled to the state machine engine.
 11. The system of claim 1, wherein the data comprises a data stream of symbols and the output data comprises one or more event vectors.
 12. A device, comprising: one or more custom compute cores configured to perform at least one preprocessing function on an input data stream to generate a preprocessed input data stream or perform at least one post-processing function on one or more event vectors read from one or more state machine engines each comprising a plurality of configurable circuits, wherein each configurable circuit of the plurality of configurable circuits comprises a data analysis circuit comprising plurality of memory components programmed with configuration data to selectively set a data state of each memory component of the plurality of memory components as one of a high state and a low state in conjunction with a pattern to be searched as an analysis of data, wherein the data analysis circuit is configured to analyze as part of the analysis of data at least a portion of the input data stream based on the data state of each memory component as selected by a respective data signal of the portion of the data and output a result as a portion of an event vector as a result of the analysis, wherein the one or more custom compute cores are implemented in fabric resources; and a direct memory access (DMA) engine configured to write the preprocessed input data stream to the one or more state machine engines subsequent to generation of the preprocessed input data stream or read the one or more event vectors from the one or more state machine engines subsequent to generation of the one or more event vectors.
 13. The device of claim 12, wherein the device is disposed on a hardware accelerator card with one or more chips each containing a respective one of the one or more state machine engines.
 14. The device of claim 12, wherein the device is coupled to a processor.
 15. The device of claim 14, wherein the device is configured to facilitate communication between a processor utilizing a peripheral component interconnect express (PCIe) interface and the one or more state machine engines utilizing a double data rate (DDR) interface.
 16. The device of claim 12, wherein the at least one preprocessing function comprises at least one of organizing the input data stream to match an architecture of the one or more state machine engines, compressing the input data stream, modifying the input data stream, segmenting the input data stream, filtering the input data stream, sorting the input data stream, deleting portions of the input data stream, or some combination thereof.
 17. The device of claim 12, wherein the at least one post-processing function comprises at least one of compressing the one or more event vectors to reduce traffic on a bus of the device and improve processing throughput performance, segmenting the one or more event vectors, modifying the one or more event vectors, filtering the one or more event vectors, sorting the one or more event vectors, deleting portions of the one or more event vectors, or some combination thereof.
 18. A method, comprising: receiving a data stream comprising symbols to be searched as part of a pattern search at an integrated circuit device; performing one or more preprocessing functions on the data stream to generate a preprocessed data stream using one or more preprocessing cores of the integrated circuit device; outputting the preprocessed data stream to a state machine engine comprising a plurality of configurable circuits, wherein each configurable circuit of the plurality of configurable circuits comprises a data analysis circuit comprising a plurality of memory components programmed with configuration data to selectively set a data state of each memory component of the plurality of memory components as one of a high state and a low state in conjunction with the pattern search, wherein the data analysis circuit is configured to analyze as part of the pattern search at least a portion of the preprocessed data stream based on the data state of each memory component as selected by a respective data signal of the at least a portion of the preprocessed data stream and output a result as a portion of an event vector as a result of the data analysis; transmitting the event vector from the state machine engine; and receiving the event vector at the integrated circuit device from the state machine engine.
 19. The method of claim 18, comprising: performing one or more post-processing functions on the event vector to generate a post-processed event vector using one or more post-processing cores of the integrated circuit device; and outputting the post-processed event vectors to a processor.
 20. The method of claim 19, wherein the one or more preprocessing cores and the one or more post-processing cores comprise instruction sets that follow an application programming interface specification defining a usable address map and physical periphery interface of the integrated circuit device for direct memory access transactions and coordination with a driver of the integrated circuit device, and the instruction sets are implemented in unused resource space of the integrated circuit device as firmware. 